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Decadz/README.md

Hi, I'm Christian! 👋

I am a research fellow at Victoria University of Wellington (VUW), working on meta-learning in deep neural networks. My current application areas are in topics related to optimization-based few-shot learning, with a specific focus on meta-learned loss functions and optimizers. For students with prior publication experience in meta-learning or related areas, I am open to co-supervision of masters and PhD projects through VUW with Dr Qi Chen and Prof Bing Xue. If you are interested please do not hesitate to contact me.

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  1. Evolved-Model-Agnostic-Loss Evolved-Model-Agnostic-Loss Public

    PyTorch code for the EvoMAL algorithm presented in "Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning" (TPAMI-2023). Paper Link: https://arxiv.org/abs/2209.08907

    Python 13

  2. Sparse-Label-Smoothing-Regularization Sparse-Label-Smoothing-Regularization Public

    PyTorch code for Sparse Label Smoothing Regularization presented in "Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning" (TPAMI-2023). Paper Link: https://arxiv.org/abs/2209.08907

    Python 1

  3. Meta-Learning-Literature-Overview Meta-Learning-Literature-Overview Public

    List of AI/ML papers related to my thesis on "Meta-Learning Loss Functions for Deep Neural Networks". Thesis link: https://arxiv.org/abs/2406.09713

    2

  4. Genetic-Programming-with-Rademacher-Complexity Genetic-Programming-with-Rademacher-Complexity Public

    Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

    Python 14 4